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import streamlit as st
import google.generativeai as genai
from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.prompts import PromptTemplate
import os
# Streamlit page configuration
st.set_page_config(page_title="AI Tool", page_icon=":robot:")
st.title("GPT Clone")
st.sidebar.title("Select your LLM Model")
# Sidebar to select the model
model = st.sidebar.selectbox("Please select any model:",
("Gemini", "Mistral", "Llama"),
placeholder="Select your LLM model...")
st.write("Your LLM Model is:", model)
# Initialize API key state
if "api_key" not in st.session_state:
st.session_state["api_key"] = ''
# Function to get API key input from the user
def get_api_key():
if model == "Gemini":
st.session_state["api_key"] = st.sidebar.text_input("Please enter your Gemini API key", type='password')
else:
st.session_state["api_key"] = st.sidebar.text_input("Please enter your HuggingFace API key", type='password')
return st.session_state["api_key"]
# Function to interact with HuggingFace models
def invoke_hugging_llm(model_name, api_key, prompt):
os.environ["HUGGINGFACEHUB_API_TOKEN"] = api_key
llm = HuggingFaceEndpoint(repo_id=model_name)
response = llm.invoke(prompt)
return response
import logging
logging.basicConfig(level=logging.DEBUG)
def get_llm_response(api_key, prompt):
logging.debug(f"Using API key: {api_key}")
logging.debug(f"Prompt: {prompt}")
try:
if model == "Mistral":
model_name = "mistralai/Mistral-7B-Instruct-v0.3"
response = invoke_hugging_llm(model_name, api_key, prompt)
elif model == "Llama":
model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
response = invoke_hugging_llm(model_name, api_key, prompt)
elif model == "Gemini":
os.environ['GOOGLE_API_KEY'] = api_key
genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
response = llm.invoke(prompt)
logging.debug(f"Gemini response: {response}")
return response.content
except Exception as e:
logging.error(f"Error invoking model: {e}")
raise e
return response
# Get API key
api_key = get_api_key()
# Display success message if API key is provided
if api_key:
st.success("API Key Acquired")
# Text input for user's question
question = st.text_input("Ask your question")
# Button to submit the question
button2 = st.button("Submit")
# Search tool integration (DuckDuckGo)
from phi.assistant import Assistant
from phi.tools.duckduckgo import DuckDuckGo
# Initialize the search tool
search_tool = Assistant(tools=[DuckDuckGo()], show_tool_calls=True)
# Check if a question has been entered
search_result = None
if question:
try:
search_result = search_tool.run(question) # Adjusted from 'print_response' to 'run'
except Exception as e:
st.error(f"Error performing search: {str(e)}")
search_result = "No search results found."
# Create the prompt using the search result
template = """You are an AI assistant. Provide relevant answers to the user's question.
The user's question is: {question}.
If the user asks about current affairs, use the DuckDuckGo search result as context.
The DuckDuckGo search result is: {search}"""
example_prompt = PromptTemplate(input_variables=["question", "search"], template=template)
prompt = example_prompt.format(question=question, search=search_result)
# If the submit button is clicked, get the response from the selected model
if button2:
if search_result:
response = get_llm_response(st.session_state["api_key"], prompt)
st.write(response)
else:
st.warning("Please enter a valid question to search.")
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